Reading a CIFTI file with a surface, and viewing it

CIFTI files organize the gray matter of the brain into “grayordinates”: vertices representing the left and right cortical surfaces, and voxels representing the subcortical gray matter structures and the cerebellum. A CIFTI file consists of two parts: (1) a NIFTI XML header which contains all the metadata including medial wall locations, subcortical structure labels, and the subcortical volumetric mask; and (2) a matrix representing all the grayordinate data. These components are read in together with read_cifti:

cii <- read_cifti(cifti_fnames["dscalar"])
cii

## Brain Structures: left cortex, right cortex  
##  left cortex: 5412 surface vertices, 2 measurements.
##  right cortex: 5434 surface vertices, 2 measurements.

cii <- read_cifti(cifti_fnames["dtseries"])
cii

## Brain Structures: left cortex, right cortex  
##  left cortex: 30424 surface vertices, 2 measurements.
##  right cortex: 30527 surface vertices, 2 measurements.

By default, read_cifti only reads in the left and right cortex data. The subcortical data can be included by using the argument brainstructures="all". Other brainstructure combinations can be specified too, e.g. brainstructures=c("left", "subcortical"). The full set of choices for brainstructures is any combination of "left", "right" and "subcortical", or "all" for all three.

The resulting object produced by read_cifti is a "xifti" with components data (the grayordinate data matrix, separated by brainstructure), meta (metadata, most of which is from the NIFTI XML header), and surf (surface geometry). The last component distinguishes a "xifti" from a CIFTI: the left and right cortical surface geometries are not included in CIFTI files, so they must be read from separate surface GIFTI files (ending in surf.gii). The surface must be compatible: the number of vertices must be the same, and each vertex in the CIFTI data must correspond to the vertex location in the corresponding GIFTI surface file. In this way, a "xifti" represents a combination of a CIFTI file with compatible GIFTI files for the cortical mesh.

We can add surfaces like so:

cii <- add_surf(cii, surfL=surfL_fname, surfR=surfR_fname)
cii

## Brain Structures: left cortex, right cortex, left surface, right surface  
##  left cortex: 30424 surface vertices, 2 measurements.
##      left surface model is present.
##  right cortex: 30527 surface vertices, 2 measurements.
##      right surface model is present.

Alternatively, we could have provided the surfaces at the outset of reading the CIFTI file:

cii <- read_cifti(cifti_fnames["dtseries"], surfL_fname=surfL_fname, surfR_fname=surfR_fname)
cii

## Brain Structures: left cortex, right cortex, left surface, right surface  
##  left cortex: 30424 surface vertices, 2 measurements.
##      left surface model is present.
##  right cortex: 30527 surface vertices, 2 measurements.
##      right surface model is present.

Let’s take a look! view_xifti_surface(cii) displays the cortical data on the surface mesh in an Open GL window using rgl. This function has several primary arguments:

• color_mode specifies the nature of the data values: "sequential", "qualitative" and "diverging". If it is not provided, a default mode that makes sense for the data will be used.
• colors specifies the color palette to use. If it is not provided, a default palette that makes sense for the color_mode is used.
• save to also save a screenshot of the window to a .png file; close_after_save to close the window after writing the image file.
• idx controls which timepoints to display.
• surfL and surfR to use a surface not included in the input "xifti". If not provided, the default surfaces included in ciftiTools are used.

Let’s see an example using each color_mode option. To reduce the size of this document, we will only show the static plots for now (see later in this document for an example of the interactive htmlwidget):

cii <- read_cifti(cifti_fnames["dscalar"])
view_xifti_surface(cii, idx=1, zlim=c(1,2))

.dscalar file; first column; sequential palette

view_xifti_surface(cii, idx=2, zlim=c(1,5), color_mode = "diverging")

.dscalar file; second column; diverging palette

dlabel <- view_xifti_surface(
  read_cifti(cifti_fnames["dlabel"]), 
  # Interactively, set qualitative_colorlegend to `TRUE`
  #   for a color legend that displays the label names.
  qualitative_colorlegend=FALSE, colorlegend_ncol=5
)

.dlabel file; first label; palette from label metadata

view_xifti_volume(cii) displays the subcortical data in slices. To view interactively in a web browser, set use_papaya=TRUE. By default, a series of slices is displayed overlaid on the MNI template. The orientation, numbers of slices, index and value range can be adjusted.

# cifti_fnames["dscalar_ones"] is the only file with subcortical data
cii <- read_cifti(cifti_fnames["dscalar_ones"], brainstructures="subcortical")
view_xifti_volume(cii)

## Values to be plotted range from 1 to 1.

Subcortical data (all ones)

# For information only, since papaya viewer cannot be opened during knitting
view_xifti_volume(cii, use_papaya = TRUE)

The "xifti" “plot” method (plot(cii)) will display the cortical data if possible, and the subcortical data otherwise.

More about the "xifti"

Support for surface geometry without data

A "surf" object can be read in using make_surf. They can be viewed with view_surf or, equivalently, their plot method. Here is the left hemisphere surface:

view_surf(surfL_fname)

Left hemisphere surface

We can additionally render the vertices and edges. Below is the right hemisphere surface. (It has been resampled so the edges and vertices are visible; see the below section on resampling.)

small_surf <- resample_surf(make_surf(surfR_fname), 4000)
plot(small_surf, edge_color="black", vertex_size=4)

Right hemisphere surface (resampled) with mesh

A "xifti" can contain surface geometry without the corresponding data; to make it, use as.xifti(surfL=make_surf(surfL_fname)).

Creating a new "xifti" and writing it

We can make a "xifti" from data using as.xifti. For example, let’s make a "xifti" from the mean image (over time) of the dtseries file. (Note that the dtseries used in this example does not truly contain fMRI timeseries data, but we use it for illustration.)

cii <- read_cifti(cifti_fnames["dtseries"])
cii_new <- as.xifti(
  cortexL = apply(cii$data$cortex_left, 1, mean),
  cortexL_mwall = cii$meta$cortex$medial_wall_mask$left,
  cortexR = apply(cii$data$cortex_right, 1, mean),
  cortexR_mwall = cii$meta$cortex$medial_wall_mask$right
)
is.xifti(cii_new)

## [1] TRUE

We can also include artifical subcortical data using the mask from "ones.dscalar.nii".

cii2 <- read_cifti(cifti_fnames["dscalar_ones"], brainstructures="subcortical")
vol <- cii2$data$subcort
vol <- vol - 1 + matrix(rnorm(nrow(vol)*ncol(vol)), nrow=nrow(vol))
cii_new <- as.xifti(
  cortexL = apply(cii$data$cortex_left, 1, mean),
  cortexL_mwall = cii$meta$cortex$medial_wall_mask$left,
  cortexR = apply(cii$data$cortex_right, 1, mean),
  cortexR_mwall = cii$meta$cortex$medial_wall_mask$right,
  subcortVol = vol,
  subcortLabs = cii2$meta$subcort$labels,
  subcortMask = cii2$meta$subcort$mask
)
is.xifti(cii_new)

## [1] TRUE

cii_new

## Brain Structures: left cortex, right cortex, subcortex  
##  left cortex: 30424 surface vertices, 1 measurements.
##  right cortex: 30527 surface vertices, 1 measurements.
##  subcortex: 31870 voxels, 1 measurements.
##      subcortical labels:
## 
##       Cortex-L       Cortex-R    Accumbens-L    Accumbens-R     Amygdala-L 
##              0              0            135            140            315 
##     Amygdala-R     Brain Stem      Caudate-L      Caudate-R   Cerebellum-L 
##            332           3472            728            755           8709 
##   Cerebellum-R Diencephalon-L Diencephalon-R  Hippocampus-L  Hippocampus-R 
##           9144            706            712            764            795 
##     Pallidum-L     Pallidum-R      Putamen-L      Putamen-R     Thalamus-L 
##            297            260           1060           1010           1288 
##     Thalamus-R 
##           1248

To visualize the cortical data of the new xifti object, we can add surface geometry with add_surf, or by providing surfaces with the surfL and surfR arguments to view_xifti_surface. We can also just use the inflated surfaces that come with ciftiTools by default:

view_xifti_surface(cii_new, title=".dtseries Mean Image", zlim=c(0,4))

.dtseries mean image

Here’s the subcortical data in sagittal view:

view_xifti_volume(cii_new, plane="sag")

## Values to be plotted range from -4.03655886957197 to 4.44151740889903.

Subcortical data, sagittal view (with random noise)

We can also write out a new CIFTI file with write_cifti! Here’s how:

out_dir <- "output"
written_cii_fname <- file.path(out_dir, "my_new_cifti.dscalar.nii")
write_cifti(cii_new, written_cii_fname)

## Writing left cortex.
## Writing right cortex.
## Writing subcortical data and labels.
## Creating CIFTI file from separated components.

# Verify that if we read the file back in, the result matches.
# Some metadata is lost or added, but beside that, the data is the same.
cii_new_copy <- read_cifti(written_cii_fname, brainstructures="all")
try(testthat::expect_equal(cii_new$data, cii_new_copy$data))

## Error : cii_new$data not equal to cii_new_copy$data.
## Component "cortex_left": Mean relative difference: 3.584765e-08
## Component "cortex_right": Mean relative difference: 3.548024e-08
## Component "subcort": Mean relative difference: 2.144467e-08

There is only a negligible difference between the original and the written-then-read copy due to rounding.

Resampling

ciftiTools can resample CIFTI files to a lower resolution. Here, we resample the 32k dtseries file to 6k vertices. We also provide the surfaces and resample them in conjunction.

resampled_cii_fname <- "my_new_resampled.dtseries.nii"
resampled_surfL_fname <- "my_resampled_surfL.surf.gii"
resampled_surfR_fname <- "my_resampled_surfR.surf.gii"
  
cii_6k <- resample_cifti(
  cifti_fnames["dtseries"], resampled_cii_fname,
  resamp_res = 6000,
  surfL_fname, surfR_fname,
  resampled_surfL_fname, resampled_surfR_fname,
  write_dir=out_dir
)

## Separating CIFTI file.
## Time difference of 4.021869 secs
## Resampling CIFTI file.
## Time difference of 7.675323 secs
## Merging components into a CIFTI file... 
## Time difference of 0.183552 secs

The new files can be viewed together with read_cifti. Let’s make this plot interactive, since the meshes are now much lower-res! Try clicking and dragging around the plot to rotate, and scrolling to zoom in and out. We’ll also use a blue background color to highlight this interactive figure within the vignette.

view_xifti_surface(
  read_cifti(cifti_fname=cii_6k["cifti"], surfL=cii_6k["surfL"], surfR=cii_6k["surfR"]), 
  zoom=7/10, bg="#d4edf5", cex.title=1.25, 
  title=".dtseries resampled to 6k", zlim=c(0,2)
)

You must enable Javascript to view this page properly.

We need to execute rgl.close() whenever an htmlwidget is made, to close the Open GL window:

rgl.close()

Resampling can also be performed while reading a file into R.

read_cifti(cifti_fnames["dscalar"], resamp_res=6000)

## Brain Structures: left cortex, right cortex  
##  left cortex: 5413 surface vertices, 2 measurements.
##  right cortex: 5434 surface vertices, 2 measurements.

Surfaces can also be resampled:

surf <- make_surf(surf=surfL_fname)
resample_surf(surf, resamp_res=6000)

## Vertices:  5762 
## Faces:  11520 
## Hemisphere:  left

and so too the surface GIFTI files:

resampled_surfL_fname <- file.path(out_dir, resampled_surfL_fname)
resample_gifti(
  surfL_fname, resampled_surfL_fname, 
  hemisphere="left", resamp_res=6000
)
make_surf(resampled_surfL_fname)

## Vertices:  5762 
## Faces:  11520 
## Hemisphere:  left

Finally, a CIFTI file can be resampled to match a template. This is not always faster than resampling without a template, but it ensures the files are in register with one another and retains additional metadata.

template_cii_fname <- file.path(out_dir, resampled_cii_fname)
target_cii_fname <- file.path(out_dir, "target.dtseries.nii")

# Since it's the same file, the result is similar, but
# the underlying resampling method may be slightly different.
resample_cifti_from_template(
  original_fname=cifti_fnames["dtseries"],
  template_fname=template_cii_fname,
  target_fname=target_cii_fname
)

try(testthat::expect_equal(
  read_cifti(template_cii_fname), read_cifti(target_cii_fname)
))

## Error : read_cifti(template_cii_fname) not equal to read_cifti(target_cii_fname).
## Component "meta": Component "cifti": Component "misc": Component "ParentProvenance": 1 string mismatch
## Component "meta": Component "cifti": Component "misc": Component "Provenance": 1 string mismatch

Other functionality

# Use default names for everything except left cortex
separated_fnames = separate_cifti(
  cifti_fnames["dscalar_ones"], brainstructures="all", 
  cortexL_fname="my_left_cortex.func.gii", write_dir=out_dir
)
separated_fnames

##                                                                            cortexL 
## "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/my_left_cortex.func.gii" 
##                                                                         ROIcortexL 
##  "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/ones_1k.ROI_L.func.gii" 
##                                                                            cortexR 
##      "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/ones_1k.R.func.gii" 
##                                                                         ROIcortexR 
##  "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/ones_1k.ROI_R.func.gii" 
##                                                                         subcortVol 
##             "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/ones_1k.nii" 
##                                                                        subcortLabs 
##      "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/ones_1k.labels.nii" 
##                                                                      ROIsubcortVol 
##         "C:\\Users\\damon\\Desktop\\ciftiTools\\vignettes\\output/ones_1k.ROI.nii"

cii <- read_cifti(cifti_fnames["dscalar"], flat=TRUE)
dim(cii)

## [1] 10846     2

cii_info <- ciftiTools::info_cifti(cifti_fnames["dlabel"])

cii <- read_cifti(cifti_fnames["dtseries"])
is.xifti(cii)

## [1] TRUE

# Make a mistake and have different numbers of columns for the left and right cortex
cii$data$cortex_left <- cii$data$cortex_left[,1,drop=FALSE]
is.xifti(cii)

## The left cortex, right cortex, and subcortical data (when present) must all have the same number of measurements (columns), but they do not.
## The column counts are: 1, 2

## "data" is invalid.

## [1] FALSE

Smooth a CIFTI

Use smooth_cifti to perform smoothing. This function works for both CIFTI files and "xifti" objects.

smoothed_cii_fname <- file.path(out_dir, "my_smoothed_cifti.dtseries.nii")
smooth_cifti(
  cifti_fnames["dtseries"], smoothed_cii_fname,
  surface_sigma=2, volume_sigma=2,
  surfL_fname=surfL_fname, surfR_fname=surfR_fname,
  subcortical_zeroes_as_NA=TRUE
)
plot(
  read_cifti(smoothed_cii_fname), 
  surfL=surfL_fname, surfR=surfR_fname, 
  title="Smoothed CIFTI", zlim=c(1,2)
)

Smoothed CIFTI

View multiple timepoints interactively

view_xifti_surface adds a slider bar if multiple columns/timepoints are requested. (The widget will not show up in the github htmlpreview, but should be visible in the downloaded file.)

view_xifti_surface(
  read_cifti(cifti_fname=cii_6k["cifti"], surfL=cii_6k["surfL"], surfR=cii_6k["surfR"]), 
  idx=1:2, bg="#ECF5D4", zlim=c(0,5)
)

rgl.close()